Last updated: 2022-03-29
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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta
# convert to correct data type
# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="wt", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")
samplemeta$methoxy = factor(samplemeta$Genotype=="MX04+", levels=c(T,F), labels = c("MX04+", "MX04-"))
samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Sex)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)
nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)Data contains a total of 649 Cells from 9. Q: Original raw datset containing only frankfurt data included 1149 cells. What were the filter criteria in the primary cell type analysis
Cells per Mouse
Cells per Strain
table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()Cells per Genotype
table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>%
as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr,
Celltype=samplemeta$Celltype, Treatment=samplemeta$Treatment
) %>%
as.data.frame() %>% display_tab()variables=c("Celltype","Sex", "Genotype_corr", "Treatment","Phase", "Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mito", "percent.ribo", "Mouse_ID")Descriptive stats across Cell type
res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)| T/NK | Microglia_0 | Microglia_1 | Microglia_2 | Microglia_3 | Microglia_4 | Microglia_5 | Granulocytes | p.overall | |
|---|---|---|---|---|---|---|---|---|---|
| N=22 | N=186 | N=164 | N=107 | N=99 | N=26 | N=23 | N=22 | ||
| Sex: | . | ||||||||
| f | 6 (27.3%) | 27 (14.5%) | 21 (12.8%) | 11 (10.3%) | 11 (11.1%) | 1 (3.85%) | 2 (8.70%) | 8 (36.4%) | |
| m | 16 (72.7%) | 159 (85.5%) | 143 (87.2%) | 96 (89.7%) | 88 (88.9%) | 25 (96.2%) | 21 (91.3%) | 14 (63.6%) | |
| Genotype_corr: | <0.001 | ||||||||
| WT | 3 (13.6%) | 66 (35.5%) | 93 (56.7%) | 51 (47.7%) | 44 (44.4%) | 15 (57.7%) | 11 (47.8%) | 6 (27.3%) | |
| APPPS1+ | 19 (86.4%) | 120 (64.5%) | 71 (43.3%) | 56 (52.3%) | 55 (55.6%) | 11 (42.3%) | 12 (52.2%) | 16 (72.7%) | |
| Treatment: | <0.001 | ||||||||
| Ctrl | 3 (13.6%) | 101 (54.3%) | 113 (68.9%) | 70 (65.4%) | 68 (68.7%) | 21 (80.8%) | 12 (52.2%) | 2 (9.09%) | |
| Stroke | 19 (86.4%) | 85 (45.7%) | 51 (31.1%) | 37 (34.6%) | 31 (31.3%) | 5 (19.2%) | 11 (47.8%) | 20 (90.9%) | |
| Phase: | 0.002 | ||||||||
| G1 | 1 (4.55%) | 79 (42.5%) | 70 (42.7%) | 59 (55.1%) | 42 (42.4%) | 12 (46.2%) | 14 (60.9%) | 5 (22.7%) | |
| G2M | 11 (50.0%) | 50 (26.9%) | 38 (23.2%) | 17 (15.9%) | 21 (21.2%) | 6 (23.1%) | 4 (17.4%) | 11 (50.0%) | |
| S | 10 (45.5%) | 57 (30.6%) | 56 (34.1%) | 31 (29.0%) | 36 (36.4%) | 8 (30.8%) | 5 (21.7%) | 6 (27.3%) | |
| Brain_region: | <0.001 | ||||||||
| Cortex | 3 (13.6%) | 101 (54.3%) | 113 (68.9%) | 70 (65.4%) | 68 (68.7%) | 21 (80.8%) | 12 (52.2%) | 2 (9.09%) | |
| Lesion | 19 (86.4%) | 85 (45.7%) | 51 (31.1%) | 37 (34.6%) | 31 (31.3%) | 5 (19.2%) | 11 (47.8%) | 20 (90.9%) | |
| nCount_RNA | 164049 (80916) | 150451 (78955) | 134224 (54802) | 170420 (76702) | 152640 (72678) | 172200 (70772) | 176087 (64299) | 144828 (65899) | 0.002 |
| pseudoaligned_reads | 165078 (81748) | 150942 (78850) | 134355 (54810) | 170750 (76639) | 152790 (72668) | 172433 (70717) | 176390 (64225) | 145316 (65972) | 0.002 |
| percent.mito | 2.23 (0.90) | 1.75 (1.17) | 1.62 (1.01) | 2.07 (1.07) | 1.72 (1.15) | 1.97 (1.02) | 2.11 (0.77) | 0.92 (1.00) | <0.001 |
| percent.ribo | 6.53 (2.72) | 2.68 (1.58) | 2.75 (1.81) | 2.44 (1.24) | 3.28 (1.67) | 2.11 (0.85) | 2.91 (1.35) | 2.01 (1.17) | <0.001 |
| Mouse_ID: | . | ||||||||
| 23#15773 | 0 (0.00%) | 7 (3.76%) | 25 (15.2%) | 12 (11.2%) | 17 (17.2%) | 4 (15.4%) | 1 (4.35%) | 0 (0.00%) | |
| 23#15774 | 0 (0.00%) | 1 (0.54%) | 30 (18.3%) | 10 (9.35%) | 6 (6.06%) | 1 (3.85%) | 1 (4.35%) | 0 (0.00%) | |
| 23#15792 | 1 (4.55%) | 11 (5.91%) | 13 (7.93%) | 9 (8.41%) | 6 (6.06%) | 6 (23.1%) | 1 (4.35%) | 0 (0.00%) | |
| 386 | 1 (4.55%) | 14 (7.53%) | 9 (5.49%) | 2 (1.87%) | 6 (6.06%) | 1 (3.85%) | 5 (21.7%) | 3 (13.6%) | |
| 387 | 11 (50.0%) | 10 (5.38%) | 5 (3.05%) | 6 (5.61%) | 5 (5.05%) | 0 (0.00%) | 1 (4.35%) | 6 (27.3%) | |
| 388 | 1 (4.55%) | 41 (22.0%) | 21 (12.8%) | 18 (16.8%) | 21 (21.2%) | 5 (19.2%) | 6 (26.1%) | 0 (0.00%) | |
| 409 | 1 (4.55%) | 33 (17.7%) | 16 (9.76%) | 18 (16.8%) | 9 (9.09%) | 3 (11.5%) | 3 (13.0%) | 3 (13.6%) | |
| 457 | 1 (4.55%) | 41 (22.0%) | 24 (14.6%) | 21 (19.6%) | 18 (18.2%) | 5 (19.2%) | 3 (13.0%) | 2 (9.09%) | |
| 461 | 6 (27.3%) | 28 (15.1%) | 21 (12.8%) | 11 (10.3%) | 11 (11.1%) | 1 (3.85%) | 2 (8.70%) | 8 (36.4%) |
export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))download data as excel file here
#get normalized counts
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()
# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]
# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>5,na.rm=T)})})
# keep RNAs with at least 10 cells with goood expression
idx=which(colSums(counts_per_celltype)>10)
counts = counts[idx,]counts %>% display_tab()Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html